(1) Field of Invention
The present invention relates to a 3D object recognition system and, more particularly, to a system that adapts 3D to 2D projections to extract features for use in 3D object recognition.
(2) Description of Related Art
The present invention is directed to a three-dimensional (3D) object recognition system. Many existing 3D object recognition techniques are in early stages of development and are typically conducted directly on 3D point cloud data.
For example, Teichman et al. describe first finding a dominant line using the RANdom Sample Consensus (RANSAC) technique, with a 3D point cloud then projected into top, side, and front views to obtain orientation invariant features (See the List of Cited Literature References below, Literature Reference No. 1). A disadvantage with using RANSAC is that it uses a random search process and, thus, may not always find the dominant line of an object. Furthermore, for rounded objects such as the human body, there is no obvious dominant line which decreases the accuracy and efficiency of such a technique.
Other techniques of the prior art require high density point cloud data for processing. For example, the well-known spin-image technique (see Literature Reference Nos. 3-6) requires high density data to estimate surface norms. The spin-image surface matching technique starts by making a histogram-based image of the local surface coordinates (i.e., a “spin-image”) and using standard image correlation techniques to identify good matches between points in the model and scene. A disadvantage with using the spin-image technique is that it requires high density data to perform surface matching. While operable in some applications, high density data is generally only obtained at close ranges. However, as can be appreciated, the ability to detect and recognize objects from longer ranges (where point clouds are sparse) is important in many 3D object recognition programs.
In other prior art (see Literature Reference Nos. 7-11), the 3D point cloud data is projected to a single 2D height image and a 2D intensity image for further processing. This 3D to 2D projection causes loss of intermediate level density and intensity information that is useful for 3D object segmentation at different heights. Thus, such techniques can be disadvantageous when attempting to identify objects at different heights.
Thus, a continuing need exists for a 3D object recognition system that improves upon the state of the art to generate multiple 2D density and intensity images to preserve most of the 3D intermediate level density and intensity information for object segmentation.